Please check

https://docs.google.com/document/d/1Va_CnqUgMtGCAO6hRUENvu4F7j2KTsWAXSBxKP0OZ0M/edit?usp=sharing

for detail information. Below is a problem set that helps you decide whether you are ready for this course (draft version, don't write yet)

https://drive.google.com/file/d/1fWoYhHQmbVyyyupOJQp73sZDZRD_PZsK/view?usp=sharing

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Econ 5166 serves as an introduction to “classical” machine learning (ML) methods such as PCA, LASSO, decision trees, random forests, and more, with a strong focus on their practical applications in social science research and business. This course is designed for students who have already completed an initial course in statistics, have some hands-on data manipulation experience, and are keen to delve into the underlying principles of machine learning.

Despite the myriad of excellent ML courses available at NTU, Econ 5166 stands apart due to two distinctive aspects. Firstly, the course emphasizes the underlying relationship between ML and statistics. It deciphers how ML, like any data-based exploratory technique, fits into the broader statistical framework. The connections between fundamental statistical concepts and classical ML methods—correlation and PCA, OLS regression and LASSO, hypothesis testing and classification, to name a few—will be illustrated. This class also serves as an opportunity to revisit statistics by exploring its core concepts (like correlation, expectations) in light of real-world applications. It's worth noting that given our focus on understanding statistical underpinnings rather than merely the methodology, we will primarily concentrate on the more traditional, accessible ML methods. Modern methods like deep learning will be conceptually addressed as an extension of what we will actually learn in class.

The second distinctive feature of this course involves a in-depth exploration of ML applications in social science research, and to a lesser extent, business. Our primary goal is to equip you with practical ML skills to tackle real-world challenges effectively. To achieve this, each method we discuss will be motivated by business applications, followed by an analysis of a research paper or one of my own research projects to demonstrate the relevance of ML. Additionally, an integral part of this course is a project assignment where you'll refine your skills in problem formulation, coding for data analysis, precise interpretation of statistical results, and effective communication of your findings. This hands-on approach not only solidifies your theoretical understanding but also enhances your ability to use ML methods in practical, real-world scenarios.